Delta Lake and Apache Spark Structured Streaming

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Questions and Answers

What is a key advantage of using change data feed to handle schema changes?

It provides a high-level overview of the operations

It only works with upsert-only Delta tables

It provides low-level details about the operations (correct)

It ignores the operations and only updates the schema

What is the writer part of Delta Lake similar to?

Any cloud-based writing

Any file-based writing (correct)

Any streaming-based writing

Any database-based writing

What is a challenge in handling schema enforcement and evolution?

Schemas are only used in Delta Lake

Schemas are subjective

Schemas are only used in data pipelines

Schemas are objective, but people's expectations are subjective (correct)

What is a benefit of using Delta Lake for schema enforcement and evolution?

It ensures data quality and trust Signup and view all the answers

What is a key concept in handling schema changes in Delta Lake?

FAL pattern and partial success Signup and view all the answers

What is Delta Lake primarily designed for?

Scalable metadata handling and unified streaming and batch data processing Signup and view all the answers

What is the primary function of Apache Spark Structured Streaming?

To process data streams in a scalable and fault-tolerant manner Signup and view all the answers

What is the purpose of schema validation in Streaming Delta Lake?

To prevent the query from starting if the schema is not compatible Signup and view all the answers

What happens when the schema is not compatible in Streaming Delta Lake?

The query will not start Signup and view all the answers

What is the purpose of the checkpoint location in Streaming Delta Lake?

To track the progress of the streaming process Signup and view all the answers

What is the reservoir in the context of Streaming Delta Lake?

A legacy name used for tables in Delta Lake Signup and view all the answers

What are in-place changes in the context of Streaming Delta Lake?

Updates made by the data provider on the table Signup and view all the answers

What type of schema changes can be handled by Streaming Delta Lake?

Both additive and non-additive schema changes Signup and view all the answers

What is the primary goal of data trust in a collaborative environment?

To have clear expectations and understanding of the data and its limitations Signup and view all the answers

What is the primary function of metadata in data governance?

To provide context and description of the data Signup and view all the answers

What is the primary benefit of using Delta Lake in data engineering?

It provides an optimized storage format for data, making it efficient for querying and analysis Signup and view all the answers

What is the primary purpose of schema conformance in data engineering?

To ensure data consistency and quality Signup and view all the answers

What is the primary function of invariant in data engineering?

To detect and handle deviations from data expectations Signup and view all the answers

What is the primary purpose of backfilling data in data engineering?

To rehydrate data after schema changes Signup and view all the answers

What is the primary difference between Apache Kafka and Spark Structured Streaming?

Kafka is more continuous, while Spark is more batch-oriented Signup and view all the answers

What is the primary purpose of micro-batching in streaming data?

To make streaming appear bounded Signup and view all the answers

What is the primary recommendation for handling failures in streaming queries?

To isolate streaming queries to prevent failures from affecting each other Signup and view all the answers

What is the primary purpose of Medallion architecture in data engineering?

To separate internal and external data domains Signup and view all the answers

Delta Lake provides a scalable and fault-tolerant stream processing engine.

False Signup and view all the answers

Streaming Delta Lake involves defining what you want to do when you want to start with version or timestamp.

True Signup and view all the answers

The checkpoint location is used to store the reservoir version.

True Signup and view all the answers

Schema validation is optional in Streaming Delta Lake.

False Signup and view all the answers

In-place changes refer to updates made by the data provider on the table that do not cause the query to fail.

False Signup and view all the answers

The writer part of Delta Lake is similar to any database-based writing.

False Signup and view all the answers

Schema changes in Streaming Delta Lake can only be additive.

False Signup and view all the answers

The reservoir is a new concept introduced in Delta Lake.

False Signup and view all the answers

Micro-batching in Streaming Delta Lake is used to process data in real-time.

True Signup and view all the answers

Delta Lake is primarily designed for batch data processing.

False Signup and view all the answers

Delta Lake provides features like schema merging and evolution to handle changes in data schemas.

True Signup and view all the answers

Data trust is essential in a collaborative environment where multiple teams work together on data projects.

True Signup and view all the answers

Medallion architecture is a data architecture pattern that separates internal and external data domains.

True Signup and view all the answers

Delta Lake provides an optimized storage format for data, making it inefficient for querying and analysis.

False Signup and view all the answers

Schema conformance is essential for maintaining data inconsistency and quality.

False Signup and view all the answers

Delta Lake supports only batch processing, making it unsuitable for a wide range of use cases.

False Signup and view all the answers

Data products are the input of data pipelines, providing value to end-users.

False Signup and view all the answers

Effective data engineering involves maintaining data distrust, governance, and quality.

False Signup and view all the answers

Backfilling data is always possible regardless of the use case and schema changes.

False Signup and view all the answers

Apache Kafka and Spark Structured Streaming have the same semantics.

False Signup and view all the answers

Study Notes

Introduction to Delta Lake and Apache Spark Structured Streaming

Delta Lake is a storage layer that provides ACID transactions, scalable metadata handling, and unified streaming and batch data processing.
Apache Spark Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine.

Streaming Delta Lake with Apache Spark Structured Streaming

Streaming Delta Lake is a way to process data in real-time using Apache Spark Structured Streaming.
It involves defining what you want to do when you want to start, such as time travel with version or timestamp, or deciding to read the most recent table version.
There are two choices for how much to take for each microbatch: by number of bytes, max files, or by default, the rating limitation is 1,000 files.
Schema validation is an important step, with additive and non-additive schemas. If the schema is not compatible, the query will not start.

Checkpoint Location and Reservoir

The checkpoint location is a crucial part of the streaming process, with a naming convention that includes the table ID and reservoir version.
The reservoir refers to the legacy name used for tables in Delta Lake, which is still present in the checkpoints.

In-Place Changes and Schema Changes

In-place changes refer to updates made by the data provider on the table, which can cause the query to fail if not handled correctly.
Schema changes can be additive (e.g., adding a new column) or non-additive (e.g., renaming a column).
To handle schema changes, you can use change data feed, which provides low-level details about the operations, or define the Delta table as upsert-only.

Writer Part

The writer part is relatively easy compared to the reader, with a similar logic to any file-based writing.
It takes the same logic as any file-based writing, with a commit metadata file creation.
There are also tricky things like FAL pattern and partial success and retries.

Schema Enforcement and Evolution

Schema enforcement and evolution are critical components of data pipelines, as they ensure data quality and trust.
Schemas are objective, but people's expectations are subjective, making it a complex issue to handle.
Delta Lake can help with schema enforcement and evolution, but it requires understanding people's expectations and constraints.
The Medallion architecture for data quality will be discussed later in the presentation.### Data Trust and Collaboration
Data trust is critical in a collaborative environment where multiple teams work together on data projects
Data trust involves having clear expectations and understanding of the data and its limitations
It is essential to establish a high-trust environment where data is shared and utilized efficiently

Data Governance and Metadata

Good metadata is essential for data governance and trust
Metadata provides context and description of the data, set
With metadata, you can understand the purpose, owner, and schema of the data
It helps in identifying data quality issues, tracking changes, and automating data pipelines

Delta Lake and Data Engineering

Delta Lake is a storage layer that provides ACID compliant transactions, snapshotting, and time travel
It helps in maintaining data consistency and allows for rollbacks and retries
Delta Lake provides an optimized storage format for data, making it efficient for querying and analysis

Streaming and Medallion Architecture

Medallion architecture is a data architecture pattern that separates internal and external data domains
Internal data domains are for raw, unprocessed data, while external data domains are for transformed and processed data
Streaming data pipelines involve ingesting data from external sources, processing, and transforming it into a consumable format
Delta Lake supports streaming and batch processing, making it suitable for a wide range of use cases

Data Conformance and Evolution

Schema conformance is essential for maintaining data consistency and quality
Delta Lake provides features like schema merging and evolution to handle changes in data schemas
It allows for automatic schema merging, but also provides options for manual schema management and data validation

Invariant and Expectations

Invariant is a concept that ensures data consistency and quality
Expectations are set around the data, and any changes or deviations from these expectations can be detected and handled
With invariant and expectations, data engineers can maintain data consistency, track changes, and automate data pipelines

Data Pipelines and Products

Data pipelines involve ingesting, processing, and transforming data into a consumable format
Data products are the output of data pipelines, providing value to end-users
Data pipelines and products are critical components of data engineering and analytics

Conclusion

Effective data engineering involves maintaining data trust, governance, and quality
Delta Lake provides features and tools to support data engineering, streaming, and analytics
By leveraging Delta Lake and following best practices, data engineers can build efficient, scalable, and reliable data pipelines and products.### Delta Lake and Streaming
Delta Lake allows for schema enforcement and alteration, making it a reliable data storage solution
Schema changes can be made using merge schema which is additive, meaning it adds new columns but doesn't drop existing ones
Overwrite can be used to rewrite the entire table, but it's not recommended as it can cause data loss
Time Travel allows for restoring previous versions of the table in case of mistakes or schema changes
When using overwrite with streaming, it's important to consider the impact on downstream consumers and the potential for data loss

Backfilling Data

Backfilling data is dependent on the use case and schema changes
If schema changes occur, it's possible to backfill data, but it's important to consider the impact on downstream consumers
Rehydrating data can be expensive and may cause more problems

Apache Kafka and Spark Structured Streaming

Apache Kafka and Spark Structured Streaming are different technologies with different semantics
Kafka is more continuous, whereas Spark is more batch-oriented
Both have their own strengths and weaknesses, and the choice between them depends on the use case
Delta Lake can be used with both Kafka and Spark Streaming to process and store data

Bounded and Unbounded Streaming

Streaming can be bounded or unbounded, depending on the use case
Micro-batching can make streaming appear bounded, but it's still continuous processing
Unbounded streaming is typically used for continuous processing of growing data

Best Practices

It's recommended to isolate streaming queries to prevent failures from affecting each other
Consider the SLA and priority of the stream when deciding how to handle failures and schema changes
Communication between teams is key when making changes to the schema or streaming pipeline

Introduction to Delta Lake and Apache Spark Structured Streaming

Delta Lake is a storage layer that provides ACID transactions, scalable metadata handling, and unified streaming and batch data processing.
Apache Spark Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine.

Streaming Delta Lake with Apache Spark Structured Streaming

Streaming Delta Lake involves defining what you want to do when you want to start, such as time travel with version or timestamp, or deciding to read the most recent table version.
There are two choices for how much to take for each microbatch: by number of bytes, max files, or by default, the rating limitation is 1,000 files.
Schema validation is an important step, with additive and non-additive schemas.

Checkpoint Location and Reservoir

The checkpoint location is a crucial part of the streaming process, with a naming convention that includes the table ID and reservoir version.
The reservoir refers to the legacy name used for tables in Delta Lake, which is still present in the checkpoints.

In-Place Changes and Schema Changes

In-place changes refer to updates made by the data provider on the table, which can cause the query to fail if not handled correctly.
Schema changes can be additive (e.g., adding a new column) or non-additive (e.g., renaming a column).
Change data feed provides low-level details about the operations.

Writer Part

The writer part is relatively easy compared to the reader, with a similar logic to any file-based writing.
It takes the same logic as any file-based writing, with a commit metadata file creation.

Schema Enforcement and Evolution

Schema enforcement and evolution are critical components of data pipelines, ensuring data quality and trust.
Schemas are objective, but people's expectations are subjective, making it a complex issue to handle.

Data Trust and Collaboration

Data trust is critical in a collaborative environment where multiple teams work together on data projects.
Data trust involves having clear expectations and understanding of the data and its limitations.

Data Governance and Metadata

Good metadata is essential for data governance and trust.
Metadata provides context and description of the data, set.
With metadata, you can understand the purpose, owner, and schema of the data.

Delta Lake and Data Engineering

Delta Lake is a storage layer that provides ACID compliant transactions, snapshotting, and time travel.
It helps in maintaining data consistency and allows for rollbacks and retries.
Delta Lake provides an optimized storage format for data, making it efficient for querying and analysis.

Streaming and Medallion Architecture

Medallion architecture is a data architecture pattern that separates internal and external data domains.
Internal data domains are for raw, unprocessed data, while external data domains are for transformed and processed data.
Streaming data pipelines involve ingesting data from external sources, processing, and transforming it into a consumable format.

Data Conformance and Evolution

Schema conformance is essential for maintaining data consistency and quality.
Delta Lake provides features like schema merging and evolution to handle changes in data schemas.

Invariant and Expectations

Invariant is a concept that ensures data consistency and quality.
Expectations are set around the data, and any changes or deviations from these expectations can be detected and handled.

Data Pipelines and Products

Data pipelines involve ingesting, processing, and transforming data into a consumable format.
Data products are the output of data pipelines, providing value to end-users.

Conclusion

Effective data engineering involves maintaining data trust, governance, and quality.
Delta Lake provides features and tools to support data engineering, streaming, and analytics.

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Description

Learn about Delta Lake, a storage layer for scalable metadata handling and unified data processing, and Apache Spark Structured Streaming, a scalable and fault-tolerant stream processing engine.